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For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss.
A generalized solution of the orthogonal procrustes problem
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Iterative quantization: A procrustean approach to learning binary codes, in: CVPR
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Semi-supervised hashing for large-scale search
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Learning binary hash codes for large-scale image search, in: Studies in Computational Intelligence
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K-means hashing: An affinity-preserving quantization method for learning binary compact codes, in: CVPR
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A feasible method for optimization with orthogonality constraints
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Fast supervised hashing with decision trees for high-dimensional data, in: CVPR
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Hashing for similarity search: A survey
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A survey on learning to hash
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Hashing with binary autoencoders, in: CVPR
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Deep hashing for compact binary codes learning, in: CVPR
Erin Liong, V., Lu, J., Wang, G., Moulin, P., Zhou, J., 2015 · 2015
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Supervised discrete hashing, in: CVPR
Shen, F., Shen, C., Liu, W., Shen, H.T., 2015 · 2015
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Learning to hash for indexing big data - a survey, in: Proceedings of the IEEE
Wang, J., Liu, W., Kumar, S., Chang, S., 2015 · 2015
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Cao, Y., Long, M., Liu, B., Wang, J., 2018 · 2018
Closest in time.
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Chen, Z., Yuan, X., Lu, J., Tian, Q., Zhou, J., 2018 · 2018
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Closest in time.
Hashing with angular reconstructive embeddings
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Binary multidimensional scaling for hashing
Huang, Y., Lin, Z., 2018 · 2018
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Unsupervised deep hashing with similarity-adaptive and discrete optimization
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Binary generative adversarial networks for image retrieval, in: AAAI
Song, J., 2018 · 2018
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A general framework for linear distance preserving hashing
Wang, M., Zhou, W., Tian, Q., Li, H., 2018 · 2018
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Compact hash code learning with binary deep neural network
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Simultaneous feature aggregating and hashing for compact binary code learning
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Deep Supervised Hashing for Fast Image Retrieval, in: CVPR, pp. 2064–2072
Liu, H., Wang, R., Shan, S., Chen, X., 2016 · 2072
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